Altmetric

A deep matrix factorization method for learning attribute representations

File Description SizeFormat 
1509.03248v1.pdfAccepted version3.19 MBAdobe PDFView/Open
Title: A deep matrix factorization method for learning attribute representations
Authors: Trigeorgis, G
Bousmalis, K
Zafeiriou, S
Schuller, B
Item Type: Journal Article
Abstract: Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies can not interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We also present a semisupervised version of the algorithm, named Deep WSF, that allows the use of (partial) prior information for each of the known attributes of a dataset, that allows the model to be used on datasets with mixed attribute knowledge. Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization, but also other state-of-the-art methodologies variants.
Issue Date: 15-Apr-2016
Date of Acceptance: 24-Feb-2016
URI: http://hdl.handle.net/10044/1/32286
DOI: https://dx.doi.org/10.1109/TPAMI.2016.2554555
ISSN: 0162-8828
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Start Page: 417
End Page: 429
Journal / Book Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 39
Issue: 3
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Funder's Grant Number: EP/J017787/1
645378
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
Semi-NMF
deep semi-NMF
unsupervised feature learning
face clustering
semi-supervised learning
Deep WSF
WSF
matrix factorization
face classification
NONNEGATIVE MATRIX
IMAGE REPRESENTATION
RECOGNITION
DIMENSIONALITY
ALGORITHMS
POSE
0801 Artificial Intelligence And Image Processing
0806 Information Systems
0906 Electrical And Electronic Engineering
Artificial Intelligence & Image Processing
Publication Status: Published
Appears in Collections:Faculty of Engineering
Computing



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx